Personalizacijaprocesaelektronskogučenjaprimenomsistemazagenerisanjepreporukazasnovanognatehnikamakolaborativnogtagovanja

The  research  topic  involves  personalization  of  an  e‐learning  system  based  on collaborative  tagging  techniques  integrated  in  a  recommender  system.  Collaborative  tagging systems allow users to upload their resources, and to label them with arbitrary words, so‐called tags.  The  systems  can  be  distinguished  according  to  what  kind  of  resources  are  supported. Besides helping user to organize his or her personal collections, a tag also can be regarded as a user’s personal opinion expression. The increasing number of users providing information about themselves  through  social  tagging  activities  caused  the  emergence  of  tag‐based  profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. Dissertation  research  aims  to  analyze  and  define  an  enhanced  model  to  select  tags  that  reveal the preferences and characteristics of users required to generate personalized recommendations. Options  on  the  use  of  models  for  personalized  tutoring  system  were  also  considered. Personalized  learning  occurs  when  e‐learning  systems  make  deliberate  efforts  to  design educational  experiences  that  fit  the  needs,  goals,  talents,  learning  styles,  interests  of  their learners  and  learners  with  similar  characteristics.  In  practice,  models  defined  in  the dissertation were evaluated on tutoring system for teaching Java programming language.Predmet istraživanja disertacije obuhvata personalizaciju tutorskih sistema za elektronsko učenje primenom tehnika kolaborativnog tagovanja (collaborative tagging techniques) integrisanih u sisteme za generisanje preporuka (recommender systems). Tagovi, kao oblik meta podataka, predstavljaju proizvoljne ključne reči ili fraze koje korisnik može da upotrebi za označavanje različitih sadržaja. Pored toga što tagovi korisnicima pružaju pomoć u organizaciji sadržaja, oni su korisni i u izražavanju mišljenja korisnika. Veliki broj informacija koje korisnici pružaju o sebi kroz aktivnosti tagovanja otvorio je mogućnost primene tagova u generisanju preporuka. Istraživanje disertacije je usmereno na analizu i definisanje poboljšanih modela za odabir tagova koji otkrivaju sklonosti i osobine korisnika potrebne za generisanje personalizovanih preporuka. Razmatrane su i mogućnosti primene tako dobijenih modela za personalizaciju tutorskih sistema. Personalizovani tutorski sistemi korisniku pružaju optimalne putanje kretanja i adekvatne aktivnosti učenja na osnovu njegovih osobina, njegovog stila učenja, znanja koje on poseduje u toj oblasti, kao i prethodnog iskustva korisnika sistema koji imaju slične karakteristike. Modeli definisani u disertaciji u praksi su evaluirani na tutorskom sistemu za učenje programskog jezika Java

Personalizacijaprocesaelektronskogučenjaprimenomsistemazagenerisanjepreporukazasnovanognatehnikamakolaborativnogtagovanja

The  research  topic  involves  personalization  of  an  e‐learning  system  based  on collaborative  tagging  techniques  integrated  in  a  recommender  system.  Collaborative  tagging systems allow users to upload their resources, and to label them with arbitrary words, so‐called tags.  The  systems  can  be  distinguished  according  to  what  kind  of  resources  are  supported. Besides helping user to organize his or her personal collections, a tag also can be regarded as a user’s personal opinion expression. The increasing number of users providing information about themselves  through  social  tagging  activities  caused  the  emergence  of  tag‐based  profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. Dissertation  research  aims  to  analyze  and  define  an  enhanced  model  to  select  tags  that  reveal the preferences and characteristics of users required to generate personalized recommendations. Options  on  the  use  of  models  for  personalized  tutoring  system  were  also  considered. Personalized  learning  occurs  when  e‐learning  systems  make  deliberate  efforts  to  design educational  experiences  that  fit  the  needs,  goals,  talents,  learning  styles,  interests  of  their learners  and  learners  with  similar  characteristics.  In  practice,  models  defined  in  the dissertation were evaluated on tutoring system for teaching Java programming language.Predmet istraživanja disertacije obuhvata personalizaciju tutorskih sistema za elektronsko učenje primenom tehnika kolaborativnog tagovanja (collaborative tagging techniques) integrisanih u sisteme za generisanje preporuka (recommender systems). Tagovi, kao oblik meta podataka, predstavljaju proizvoljne ključne reči ili fraze koje korisnik može da upotrebi za označavanje različitih sadržaja. Pored toga što tagovi korisnicima pružaju pomoć u organizaciji sadržaja, oni su korisni i u izražavanju mišljenja korisnika. Veliki broj informacija koje korisnici pružaju o sebi kroz aktivnosti tagovanja otvorio je mogućnost primene tagova u generisanju preporuka. Istraživanje disertacije je usmereno na analizu i definisanje poboljšanih modela za odabir tagova koji otkrivaju sklonosti i osobine korisnika potrebne za generisanje personalizovanih preporuka. Razmatrane su i mogućnosti primene tako dobijenih modela za personalizaciju tutorskih sistema. Personalizovani tutorski sistemi korisniku pružaju optimalne putanje kretanja i adekvatne aktivnosti učenja na osnovu njegovih osobina, njegovog stila učenja, znanja koje on poseduje u toj oblasti, kao i prethodnog iskustva korisnika sistema koji imaju slične karakteristike. Modeli definisani u disertaciji u praksi su evaluirani na tutorskom sistemu za učenje programskog jezika Java

Applying Recommender Systems and Adaptive Hypermedia for e-Learning Personalizatio

Learners learn differently because they are different -- and they grow more distinctive as they mature. Personalized learning occurs when e-learning systems make deliberate efforts to design educational experiences that fit the needs, goals, talents, and interests of their learners. Researchers had recently begun to investigate various techniques to help teachers improve e-learning systems. In this paper we present our design and implementation of an adaptive and intelligent web-based programming tutoring system -- Protus, which applies recommendation and adaptive hypermedia techniques. This system aims at automatically guiding the learner's activities and recommend relevant links and actions to him/her during the learning process. Experiments on real data sets show the suitability of using both recommendation and hypermedia techniques in order to suggest online learning activities to learners based on their preferences, knowledge and the opinions of the users with similar characteristics

Milyen tényezők befolyásolják az általános iskolai tanárok IKT-használatát Szerbiában?

Az információs és kommunikációs technológia (IKT) és az Internet használata fontos szerepet tölt be a tanításban mind a tanárok, mind a tanulók számára, hiszen nagyban hozzájárul az egész életen át tartó tanuláshoz és a személyes fejlődéshez. A tanulmány célja az IKT, a multimédia technológia és az Internet használatának vizsgálata a szerbiai általános iskolákban. A kutatás 66 egy-, két- és háromnyelvű általános iskolában zajlott a Vajdasági Autonóm Tartomány területén a 2018/2019-es tanévben, melyben a természettudományi, a nyelvi- és a humán tudományok tanárai vettek részt. Összehasonlítottuk a különböző társadalmi-demográfiai jellemzőkkel bíró tanárok IKT-használati szokásait, beleértve a tananyag elkészítését is. Továbbá vizsgáltuk az IKT-használat gyakoriságát a tanórákon, melyet öt tényező befolyásol leginkább: a tanárok munkatapasztalata, a tanórákra való felkészülés közbeni IKT-használat, a kor, a nem és a szakterület

Recommending Learning Videos for MOOCs and Flipped Classrooms

[EN] New teaching approaches are emerging in higher education, such as flipped classrooms. In addition, academic institutions are offering new types of training like Massive Online Open Courses. Both of these new ways of education require high-quality learning objects for their success, with learning videos being the most common to provide theoretical concepts. This paper describes a hybrid learning recommender system based on content-based techniques, which is able to recommend useful videos to learners and teachers from a learning video repository. This hybrid technique has been successfully applied to a real scenario such as the central video repository of the Universitat Politècnica de València.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 and TIN2017-89156-R projects of the Spanish government, and PROMETEO/2018/002 project of Generalitat Valenciana. J. Jordán and V. Botti are funded by UPV PAID-06-18 project. J. Jordán is also funded by grant APOSTD/2018/010 of Generalitat Valenciana - Fondo Social Europeo.Jordán, J.; Valero Cubas, S.; Turró, C.; Botti Navarro, VJ. (2020). Recommending Learning Videos for MOOCs and Flipped Classrooms. Springer. 146-157. https://doi.org/10.1007/978-3-030-49778-1_12S146157Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Knowl.-Based Syst. 22(4), 261–265 (2009)Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)Chen, W., Niu, Z., Zhao, X., Li, Y.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2), 271–284 (2012). https://doi.org/10.1007/s11280-012-0187-zvan Dijck, J., Poell, T.: Higher education in a networked world: European responses to U.S. MOOCs. Int. J. Commun.: IJoC 9, 2674–2692 (2015)Dwivedi, P., Bharadwaj, K.K.: e-learning recommender system for a group of learners based on the unified learner profile approach. Expert Syst. 32(2), 264–276 (2015)Herlocker, J., Konstan, J., Terveen, L., Riedl, J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)Institute and Committee of Electrical and Electronics Engineers: Learning Technology Standards: IEEE Standard for Learning Object Metadata. IEEE Standard 1484.12.1 (2002)Klašnja-Milićević, A., Ivanović, M., Nanopoulos, A.: Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif. Intell. Rev. 44(4), 571–604 (2015). https://doi.org/10.1007/s10462-015-9440-zMaassen, P., Nerland, M., Yates, L. (eds.): Reconfiguring Knowledge in Higher Education. Higher Education Dynamics, vol. 50. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-72832-2MLLP research group, Universitat Politècnica de València: Tlp: The translectures-upv platform. http://www.mllp.upv.es/tlpO’Flaherty, J., Phillips, C.: The use of flipped classrooms in higher education: a scoping review. Internet High. Educ. 25, 85–95 (2015)Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click-through rate for new ads. In: Proceedings of the 16th international conference on World Wide Web, pp. 521–530 (2007)Rodríguez, P., Heras, S., Palanca, J., Duque, N., Julián, V.: Argumentation-based hybrid recommender system for recommending learning objects. In: Rovatsos, M., Vouros, G., Julian, V. (eds.) EUMAS/AT -2015. LNCS (LNAI), vol. 9571, pp. 234–248. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33509-4_19Roehl, A., Reddy, S.L., Shannon, G.J.: The flipped classroom: an opportunity to engage millennial students through active learning strategies. J. Fam. Consum. Sci. 105, 44–49 (2013)Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)Stoica, A.S., Heras, S., Palanca, J., Julian, V., Mihaescu, M.C.: A semi-supervised method to classify educational videos. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 218–228. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_19Tarus, J.K., Niu, Z., Yousif, A.: A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Gener. Comput. Syst. 72, 37–48 (2017)Tucker, B.: The flipped classroom. Online instruction at home frees class time for learning. Educ. Next Winter 2012, 82–83 (2012)Turcu, G., Heras, S., Palanca, J., Julian, V., Mihaescu, M.C.: Towards a custom designed mechanism for indexing and retrieving video transcripts. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 299–309. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_26Turró, C., Morales, J.C., Busquets-Mataix, J.: A study on assessment results in a large scale flipped teaching experience. In: 4th International Conference on Higher Education Advances (HEAD 2018), pp. 1039–1048 (2018)Turró, C., Despujol, I., Busquets, J.: Networked teaching, the story of a success on creating e-learning content at Universitat Politècnica de València. EUNIS J. High. Educ. (2014)Zajda, J., Rust, V. (eds.): Globalisation and Higher Education Reforms. GCEPR, vol. 15. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28191-

A novel algorithm for dynamic student profile adaptation based on learning styles

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method

E-learning Personalization Systems and Sustainable Education

In the World Declaration on Higher Education, the concept of higher education is defined as “all types of studies, training or research training at the postsecondary level, provided by universities or other educational establishments that are approved as institutions of higher education by the competent state authorities” [...

Social tagging strategy for enhancing e-learning experience

Success of e-learning systems depends on their capability to automatically retrieve and recommend relevant learning content according to the preferences of a specific learner. Learning experience and dynamic choice of educational material that is presented to learners can be enhanced using different recommendation techniques. As popularity of collaborative tagging systems grows, users’ tags could provide useful information to improve recommender system algorithms in e-learning environments. In this paper, we present an approach for implementation of collaborative tagging techniques into online tutoring system. The implemented approach combines social tagging and sequential patterns mining for generating recommendations of learning resources to learners. Several experiments were carried out in order to verify usability of the proposed hybrid method within e-learning environment and analyze selected social tagging techniques

Assessing learning styles through eye tracking for e-learning applications

Adapting the presentation of learning material to the specific student’s characteristics is useful to improve the overall learning experience and learning styles can play an important role to this purpose. In this paper, we investigate the possibility to distinguish between Visual and Verbal learning styles from gaze data. In an experiment involving first year students of an engineering faculty, content regarding the basics of programming was presented in both text and graphic form, and participants’ gaze data was recorded by means of an eye tracker. Three metrics were selected to characterize the user’s gaze behavior, namely, percentage of fixation duration, percentage of fixations, and average fixation duration. Percentages were calculated on ten intervals into which each participant’s interaction time was subdivided, and this allowed us to perform timebased assessments. The obtained results showed a significant relation between gaze data and Visual/Verbal learning styles for an information arrangement where the same concept is presented in graphical format on the left and in text format on the right. We think that this study can provide a useful contribution to learning styles research carried out exploiting eye tracking technology, as it is characterized by unique traits that cannot be found in similar investigations